Overview

Dataset statistics

Number of variables23
Number of observations4061
Missing cells7755
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory729.8 KiB
Average record size in memory184.0 B

Variable types

Numeric13
Categorical10

Alerts

Age is highly overall correlated with Creatinine_ClearanceHigh correlation
Year_DM_Diagnosed is highly overall correlated with DM_DurationHigh correlation
DM_Duration is highly overall correlated with Year_DM_DiagnosedHigh correlation
Waist is highly overall correlated with BMIHigh correlation
Fasting_Blood_Glucose_Value_SI_Units is highly overall correlated with HbA1C_Admission_ValueHigh correlation
HbA1C_Admission_Value is highly overall correlated with Fasting_Blood_Glucose_Value_SI_Units and 1 other fieldsHigh correlation
BMI is highly overall correlated with WaistHigh correlation
Creatinine_Clearance is highly overall correlated with AgeHigh correlation
Education is highly overall correlated with Work and 6 other fieldsHigh correlation
DM_Treatment is highly overall correlated with HbA1C_Admission_Value and 2 other fieldsHigh correlation
Work is highly overall correlated with Education and 7 other fieldsHigh correlation
Cardiac_Arrest_Admission is highly overall correlated with Education and 6 other fieldsHigh correlation
Non_Cardiac_Condition is highly overall correlated with Education and 7 other fieldsHigh correlation
Hypertension is highly overall correlated with Education and 7 other fieldsHigh correlation
Dyslipidemia is highly overall correlated with Education and 7 other fieldsHigh correlation
DM is highly overall correlated with Education and 9 other fieldsHigh correlation
DM_Type is highly overall correlated with DM_Treatment and 1 other fieldsHigh correlation
Smoking_History is highly overall correlated with Work and 5 other fieldsHigh correlation
Lipid_24_Collected is highly overall correlated with Education and 7 other fieldsHigh correlation
Cardiac_Arrest_Admission is highly imbalanced (91.7%)Imbalance
Non_Cardiac_Condition is highly imbalanced (87.6%)Imbalance
Lipid_24_Collected is highly imbalanced (60.8%)Imbalance
Year_DM_Diagnosed has 2057 (50.7%) missing valuesMissing
DM_Duration has 1975 (48.6%) missing valuesMissing
Waist has 92 (2.3%) missing valuesMissing
Fasting_Blood_Glucose_Value_SI_Units has 1289 (31.7%) missing valuesMissing
HbA1C_Admission_Value has 1499 (36.9%) missing valuesMissing
Cholesterol_Value_SI_Units has 334 (8.2%) missing valuesMissing
Triglycerides_Value_SI_Units has 374 (9.2%) missing valuesMissing
BMI has 60 (1.5%) missing valuesMissing
Creatinine_Clearance has 69 (1.7%) missing valuesMissing
Year_DM_Diagnosed is highly skewed (γ1 = -30.88489352)Skewed
DM_Duration is highly skewed (γ1 = 31.49782961)Skewed
Cholesterol_Value_SI_Units is highly skewed (γ1 = 44.88015296)Skewed
Triglycerides_Value_SI_Units is highly skewed (γ1 = 22.38220136)Skewed
Education has 969 (23.9%) zerosZeros
DM_Treatment has 103 (2.5%) zerosZeros

Reproduction

Analysis started2023-02-28 17:25:32.430238
Analysis finished2023-02-28 17:26:01.957407
Duration29.53 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct79
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.351884
Minimum18
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-02-28T17:26:02.094519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile40
Q152
median60
Q369
95-th percentile81
Maximum112
Range94
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.727595
Coefficient of variation (CV)0.21088977
Kurtosis-0.12256521
Mean60.351884
Median Absolute Deviation (MAD)9
Skewness0.0038224183
Sum245089
Variance161.99166
MonotonicityNot monotonic
2023-02-28T17:26:02.293027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 149
 
3.7%
72 138
 
3.4%
60 132
 
3.3%
57 132
 
3.3%
67 121
 
3.0%
59 119
 
2.9%
52 117
 
2.9%
56 114
 
2.8%
64 112
 
2.8%
70 111
 
2.7%
Other values (69) 2816
69.3%
ValueCountFrequency (%)
18 1
 
< 0.1%
23 1
 
< 0.1%
24 3
0.1%
25 6
0.1%
26 5
0.1%
27 4
0.1%
28 5
0.1%
29 5
0.1%
30 5
0.1%
31 7
0.2%
ValueCountFrequency (%)
112 1
 
< 0.1%
102 1
 
< 0.1%
99 5
0.1%
97 2
 
< 0.1%
96 1
 
< 0.1%
95 3
0.1%
94 6
0.1%
93 6
0.1%
92 6
0.1%
91 3
0.1%

Year_DM_Diagnosed
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct46
Distinct (%)2.3%
Missing2057
Missing (%)50.7%
Infinite0
Infinite (%)0.0%
Mean1998.4251
Minimum10
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-02-28T17:26:02.472385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile1984.15
Q11996
median2002
Q32007
95-th percentile2011
Maximum2022
Range2012
Interquartile range (IQR)11

Descriptive statistics

Standard deviation63.20694
Coefficient of variation (CV)0.031628375
Kurtosis968.11143
Mean1998.4251
Median Absolute Deviation (MAD)5
Skewness-30.884894
Sum4004844
Variance3995.1172
MonotonicityNot monotonic
2023-02-28T17:26:02.661997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
2002 283
 
7.0%
2007 162
 
4.0%
1992 150
 
3.7%
1997 149
 
3.7%
2000 114
 
2.8%
2008 106
 
2.6%
2006 97
 
2.4%
2011 76
 
1.9%
2010 74
 
1.8%
2009 68
 
1.7%
Other values (36) 725
 
17.9%
(Missing) 2057
50.7%
ValueCountFrequency (%)
10 1
 
< 0.1%
20 1
 
< 0.1%
1967 1
 
< 0.1%
1970 1
 
< 0.1%
1972 2
 
< 0.1%
1973 2
 
< 0.1%
1974 1
 
< 0.1%
1975 1
 
< 0.1%
1977 7
0.2%
1978 1
 
< 0.1%
ValueCountFrequency (%)
2022 1
 
< 0.1%
2013 1
 
< 0.1%
2012 52
 
1.3%
2011 76
1.9%
2010 74
1.8%
2009 68
1.7%
2008 106
2.6%
2007 162
4.0%
2006 97
2.4%
2005 65
1.6%

DM_Duration
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct45
Distinct (%)2.2%
Missing1975
Missing (%)48.6%
Infinite0
Infinite (%)0.0%
Mean13.706616
Minimum-10
Maximum2004
Zeros30
Zeros (%)0.7%
Negative1
Negative (%)< 0.1%
Memory size31.9 KiB
2023-02-28T17:26:02.858747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile1
Q15.25
median10
Q316
95-th percentile27.75
Maximum2004
Range2014
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation61.986548
Coefficient of variation (CV)4.5223817
Kurtosis1007.2521
Mean13.706616
Median Absolute Deviation (MAD)5
Skewness31.49783
Sum28592
Variance3842.3321
MonotonicityNot monotonic
2023-02-28T17:26:03.011230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
10 339
 
8.3%
20 198
 
4.9%
15 170
 
4.2%
5 169
 
4.2%
12 113
 
2.8%
6 100
 
2.5%
4 85
 
2.1%
2 83
 
2.0%
7 82
 
2.0%
8 79
 
1.9%
Other values (35) 668
 
16.4%
(Missing) 1975
48.6%
ValueCountFrequency (%)
-10 1
 
< 0.1%
0 30
 
0.7%
1 78
1.9%
2 83
2.0%
3 76
1.9%
4 85
2.1%
5 169
4.2%
6 100
2.5%
7 82
2.0%
8 79
1.9%
ValueCountFrequency (%)
2004 1
 
< 0.1%
1992 1
 
< 0.1%
45 1
 
< 0.1%
42 1
 
< 0.1%
40 5
0.1%
38 1
 
< 0.1%
37 2
 
< 0.1%
36 1
 
< 0.1%
35 7
0.2%
34 1
 
< 0.1%

Heart_Rate
Real number (ℝ)

Distinct140
Distinct (%)3.5%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean85.175832
Minimum0
Maximum222
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-02-28T17:26:03.174735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57
Q171
median82
Q396
95-th percentile120
Maximum222
Range222
Interquartile range (IQR)25

Descriptive statistics

Standard deviation21.087922
Coefficient of variation (CV)0.24758105
Kurtosis3.2637604
Mean85.175832
Median Absolute Deviation (MAD)12
Skewness1.0140859
Sum345388
Variance444.70045
MonotonicityNot monotonic
2023-02-28T17:26:03.321679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 298
 
7.3%
90 203
 
5.0%
70 202
 
5.0%
100 151
 
3.7%
78 136
 
3.3%
88 126
 
3.1%
75 118
 
2.9%
72 105
 
2.6%
85 101
 
2.5%
76 88
 
2.2%
Other values (130) 2527
62.2%
ValueCountFrequency (%)
0 5
0.1%
8 1
 
< 0.1%
25 2
 
< 0.1%
27 1
 
< 0.1%
30 3
 
0.1%
34 2
 
< 0.1%
35 3
 
0.1%
36 2
 
< 0.1%
38 5
0.1%
40 11
0.3%
ValueCountFrequency (%)
222 1
 
< 0.1%
220 1
 
< 0.1%
200 2
< 0.1%
187 1
 
< 0.1%
180 4
0.1%
175 1
 
< 0.1%
174 1
 
< 0.1%
172 2
< 0.1%
170 3
0.1%
168 3
0.1%

Waist
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct120
Distinct (%)3.0%
Missing92
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean97.592341
Minimum25
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-02-28T17:26:03.498202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile72
Q188
median98
Q3109
95-th percentile125
Maximum194
Range169
Interquartile range (IQR)21

Descriptive statistics

Standard deviation17.450379
Coefficient of variation (CV)0.1788089
Kurtosis2.3121858
Mean97.592341
Median Absolute Deviation (MAD)10
Skewness-0.35062131
Sum387344
Variance304.51573
MonotonicityNot monotonic
2023-02-28T17:26:03.683940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 225
 
5.5%
100 171
 
4.2%
85 158
 
3.9%
110 151
 
3.7%
98 145
 
3.6%
105 132
 
3.3%
80 127
 
3.1%
95 123
 
3.0%
88 123
 
3.0%
102 111
 
2.7%
Other values (110) 2503
61.6%
ValueCountFrequency (%)
25 1
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
30 2
 
< 0.1%
32 9
0.2%
33 4
0.1%
34 5
0.1%
35 1
 
< 0.1%
36 8
0.2%
37 1
 
< 0.1%
ValueCountFrequency (%)
194 1
 
< 0.1%
191 2
 
< 0.1%
165 1
 
< 0.1%
160 1
 
< 0.1%
155 1
 
< 0.1%
152 1
 
< 0.1%
150 4
0.1%
148 5
0.1%
147 1
 
< 0.1%
146 1
 
< 0.1%

Fasting_Blood_Glucose_Value_SI_Units
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct458
Distinct (%)16.5%
Missing1289
Missing (%)31.7%
Infinite0
Infinite (%)0.0%
Mean8.3931782
Minimum0.33
Maximum235
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-02-28T17:26:03.879473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile4.4
Q15.5
median6.8
Q39.5
95-th percentile15.6
Maximum235
Range234.67
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.1325037
Coefficient of variation (CV)1.0880865
Kurtosis329.30432
Mean8.3931782
Median Absolute Deviation (MAD)1.6
Skewness15.946832
Sum23265.89
Variance83.402625
MonotonicityNot monotonic
2023-02-28T17:26:04.150713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 79
 
1.9%
5.2 76
 
1.9%
5.6 68
 
1.7%
5 68
 
1.7%
4.8 55
 
1.4%
5.4 53
 
1.3%
6.2 53
 
1.3%
5.8 53
 
1.3%
5.5 52
 
1.3%
5.7 49
 
1.2%
Other values (448) 2166
53.3%
(Missing) 1289
31.7%
ValueCountFrequency (%)
0.33 1
< 0.1%
0.38 1
< 0.1%
0.46 1
< 0.1%
0.47 1
< 0.1%
0.58 1
< 0.1%
0.61 1
< 0.1%
0.71 1
< 0.1%
0.81 1
< 0.1%
0.98 1
< 0.1%
1.01 1
< 0.1%
ValueCountFrequency (%)
235 1
< 0.1%
230 1
< 0.1%
156 1
< 0.1%
141 1
< 0.1%
135 1
< 0.1%
107 1
< 0.1%
100 1
< 0.1%
99 1
< 0.1%
87 1
< 0.1%
81 1
< 0.1%

HbA1C_Admission_Value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct384
Distinct (%)15.0%
Missing1499
Missing (%)36.9%
Infinite0
Infinite (%)0.0%
Mean7.5797931
Minimum0
Maximum88.4
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-02-28T17:26:04.354818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.9
Q15.9
median7
Q38.9225
95-th percentile11.9095
Maximum88.4
Range88.4
Interquartile range (IQR)3.0225

Descriptive statistics

Standard deviation2.7954479
Coefficient of variation (CV)0.36880267
Kurtosis272.19291
Mean7.5797931
Median Absolute Deviation (MAD)1.37
Skewness9.9580976
Sum19419.43
Variance7.814529
MonotonicityNot monotonic
2023-02-28T17:26:04.524506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 144
 
3.5%
5.9 71
 
1.7%
5.7 69
 
1.7%
5.8 65
 
1.6%
5.3 53
 
1.3%
5.6 52
 
1.3%
5.5 52
 
1.3%
7 49
 
1.2%
5 48
 
1.2%
5.4 48
 
1.2%
Other values (374) 1911
47.1%
(Missing) 1499
36.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
3.2 1
 
< 0.1%
3.3 1
 
< 0.1%
3.5 1
 
< 0.1%
3.6 1
 
< 0.1%
3.75 1
 
< 0.1%
3.9 12
0.3%
4 23
0.6%
4.01 1
 
< 0.1%
4.1 4
 
0.1%
ValueCountFrequency (%)
88.4 1
< 0.1%
20 1
< 0.1%
17.5 1
< 0.1%
16.6 1
< 0.1%
16 2
< 0.1%
15.8 1
< 0.1%
15.7 2
< 0.1%
15.4 1
< 0.1%
15.3 1
< 0.1%
15.2 2
< 0.1%

Cholesterol_Value_SI_Units
Real number (ℝ)

MISSING  SKEWED 

Distinct555
Distinct (%)14.9%
Missing334
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean4.9357773
Minimum0.07
Maximum489
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-02-28T17:26:04.709518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile2.83
Q13.8
median4.6
Q35.505
95-th percentile7
Maximum489
Range488.93
Interquartile range (IQR)1.705

Descriptive statistics

Standard deviation9.4548638
Coefficient of variation (CV)1.9155775
Kurtosis2127.6917
Mean4.9357773
Median Absolute Deviation (MAD)0.88
Skewness44.880153
Sum18395.642
Variance89.394449
MonotonicityNot monotonic
2023-02-28T17:26:04.893415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3 82
 
2.0%
4.5 80
 
2.0%
5.2 74
 
1.8%
4.6 73
 
1.8%
4.2 73
 
1.8%
5.6 70
 
1.7%
4.1 67
 
1.6%
4.9 67
 
1.6%
4 66
 
1.6%
4.8 65
 
1.6%
Other values (545) 3010
74.1%
(Missing) 334
 
8.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.08 3
0.1%
0.1 2
< 0.1%
0.13 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 2
< 0.1%
0.18 1
 
< 0.1%
0.7 1
 
< 0.1%
1 1
 
< 0.1%
1.2 1
 
< 0.1%
ValueCountFrequency (%)
489 1
< 0.1%
308 1
< 0.1%
15.7 1
< 0.1%
13.74 1
< 0.1%
11.8 2
< 0.1%
11.36 1
< 0.1%
11.29 1
< 0.1%
10.97 1
< 0.1%
10.12 1
< 0.1%
10 1
< 0.1%

Triglycerides_Value_SI_Units
Real number (ℝ)

MISSING  SKEWED 

Distinct432
Distinct (%)11.7%
Missing374
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean1.6739626
Minimum0.01
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-02-28T17:26:05.059456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.59
Q10.96
median1.4
Q32
95-th percentile3.6
Maximum71
Range70.99
Interquartile range (IQR)1.04

Descriptive statistics

Standard deviation1.6272688
Coefficient of variation (CV)0.97210585
Kurtosis901.04309
Mean1.6739626
Median Absolute Deviation (MAD)0.5
Skewness22.382201
Sum6171.9
Variance2.6480038
MonotonicityNot monotonic
2023-02-28T17:26:05.234864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 108
 
2.7%
1.1 107
 
2.6%
1.2 107
 
2.6%
1.3 102
 
2.5%
0.8 99
 
2.4%
0.9 98
 
2.4%
1.5 97
 
2.4%
1.4 91
 
2.2%
1.6 91
 
2.2%
1.8 80
 
2.0%
Other values (422) 2707
66.7%
(Missing) 374
 
9.2%
ValueCountFrequency (%)
0.01 6
0.1%
0.02 3
0.1%
0.03 1
 
< 0.1%
0.04 1
 
< 0.1%
0.1 1
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
0.18 1
 
< 0.1%
0.2 1
 
< 0.1%
0.25 1
 
< 0.1%
ValueCountFrequency (%)
71 1
< 0.1%
17.6 1
< 0.1%
17.4 1
< 0.1%
14.55 1
< 0.1%
11.56 1
< 0.1%
11.01 1
< 0.1%
10.59 1
< 0.1%
9.72 1
< 0.1%
9.14 1
< 0.1%
9 1
< 0.1%

BMI
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1187
Distinct (%)29.7%
Missing60
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean29.011505
Minimum13.34
Maximum342.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-02-28T17:26:05.443308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13.34
5-th percentile21.48
Q124.91
median27.76
Q331.59
95-th percentile39.54
Maximum342.78
Range329.44
Interquartile range (IQR)6.68

Descriptive statistics

Standard deviation8.9093348
Coefficient of variation (CV)0.30709661
Kurtosis507.7302
Mean29.011505
Median Absolute Deviation (MAD)3.3
Skewness16.91749
Sum116075.03
Variance79.376246
MonotonicityNot monotonic
2023-02-28T17:26:05.607032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.71 40
 
1.0%
27.68 38
 
0.9%
24.22 36
 
0.9%
29.41 30
 
0.7%
29.76 28
 
0.7%
31.25 27
 
0.7%
27.34 27
 
0.7%
25.95 26
 
0.6%
31.22 25
 
0.6%
28.73 23
 
0.6%
Other values (1177) 3701
91.1%
(Missing) 60
 
1.5%
ValueCountFrequency (%)
13.34 1
< 0.1%
14.84 1
< 0.1%
15.78 1
< 0.1%
16.42 2
< 0.1%
16.44 1
< 0.1%
16.77 1
< 0.1%
16.82 2
< 0.1%
16.9 1
< 0.1%
17.04 1
< 0.1%
17.07 1
< 0.1%
ValueCountFrequency (%)
342.78 1
< 0.1%
233.4 1
< 0.1%
221.14 1
< 0.1%
96.88 1
< 0.1%
69.25 1
< 0.1%
66.33 1
< 0.1%
65.38 1
< 0.1%
65.02 1
< 0.1%
63.57 1
< 0.1%
61.33 1
< 0.1%

Creatinine_Clearance
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3433
Distinct (%)86.0%
Missing69
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean88.192402
Minimum0.29
Maximum1164.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-02-28T17:26:05.778224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.29
5-th percentile24.502
Q156.8725
median82.84
Q3110.98
95-th percentile167.2385
Maximum1164.7
Range1164.41
Interquartile range (IQR)54.1075

Descriptive statistics

Standard deviation53.345335
Coefficient of variation (CV)0.6048745
Kurtosis100.44634
Mean88.192402
Median Absolute Deviation (MAD)26.99
Skewness6.0828736
Sum352064.07
Variance2845.7248
MonotonicityNot monotonic
2023-02-28T17:26:05.945570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.1 6
 
0.1%
83.2 6
 
0.1%
76.16 4
 
0.1%
97.17 4
 
0.1%
67.65 4
 
0.1%
76.26 4
 
0.1%
110.7 4
 
0.1%
79.9 4
 
0.1%
115.96 3
 
0.1%
98.4 3
 
0.1%
Other values (3423) 3950
97.3%
(Missing) 69
 
1.7%
ValueCountFrequency (%)
0.29 1
< 0.1%
0.52 1
< 0.1%
0.55 1
< 0.1%
0.58 1
< 0.1%
0.67 1
< 0.1%
0.78 1
< 0.1%
0.81 1
< 0.1%
0.98 1
< 0.1%
0.99 1
< 0.1%
1 1
< 0.1%
ValueCountFrequency (%)
1164.7 1
< 0.1%
1153.62 1
< 0.1%
875.43 1
< 0.1%
684.96 1
< 0.1%
626.18 1
< 0.1%
500.2 1
< 0.1%
460.63 1
< 0.1%
363.73 1
< 0.1%
321.8 1
< 0.1%
298.68 1
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
2697 
0
1364 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 2697
66.4%
0 1364
33.6%

Length

2023-02-28T17:26:06.081089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:26:06.231314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2697
66.4%
0 1364
33.6%

Most occurring characters

ValueCountFrequency (%)
1 2697
66.4%
0 1364
33.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2697
66.4%
0 1364
33.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2697
66.4%
0 1364
33.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2697
66.4%
0 1364
33.6%

Education
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9202167
Minimum0
Maximum6
Zeros969
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-02-28T17:26:06.345727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4284366
Coefficient of variation (CV)0.74389343
Kurtosis-0.55103223
Mean1.9202167
Median Absolute Deviation (MAD)1
Skewness0.32913589
Sum7798
Variance2.0404311
MonotonicityNot monotonic
2023-02-28T17:26:06.439807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 1965
48.4%
0 969
23.9%
4 645
 
15.9%
1 249
 
6.1%
5 164
 
4.0%
3 65
 
1.6%
6 4
 
0.1%
ValueCountFrequency (%)
0 969
23.9%
1 249
 
6.1%
2 1965
48.4%
3 65
 
1.6%
4 645
 
15.9%
5 164
 
4.0%
6 4
 
0.1%
ValueCountFrequency (%)
6 4
 
0.1%
5 164
 
4.0%
4 645
 
15.9%
3 65
 
1.6%
2 1965
48.4%
1 249
 
6.1%
0 969
23.9%

Work
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
0
2923 
1
994 
2
 
140
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row3
5th row3

Common Values

ValueCountFrequency (%)
0 2923
72.0%
1 994
 
24.5%
2 140
 
3.4%
3 4
 
0.1%

Length

2023-02-28T17:26:06.573642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:26:06.711126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2923
72.0%
1 994
 
24.5%
2 140
 
3.4%
3 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2923
72.0%
1 994
 
24.5%
2 140
 
3.4%
3 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2923
72.0%
1 994
 
24.5%
2 140
 
3.4%
3 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2923
72.0%
1 994
 
24.5%
2 140
 
3.4%
3 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2923
72.0%
1 994
 
24.5%
2 140
 
3.4%
3 4
 
0.1%

Cardiac_Arrest_Admission
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
0
3974 
1
 
73
2
 
10
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row3
5th row3

Common Values

ValueCountFrequency (%)
0 3974
97.9%
1 73
 
1.8%
2 10
 
0.2%
3 4
 
0.1%

Length

2023-02-28T17:26:06.856840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:26:07.009091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3974
97.9%
1 73
 
1.8%
2 10
 
0.2%
3 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3974
97.9%
1 73
 
1.8%
2 10
 
0.2%
3 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3974
97.9%
1 73
 
1.8%
2 10
 
0.2%
3 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3974
97.9%
1 73
 
1.8%
2 10
 
0.2%
3 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3974
97.9%
1 73
 
1.8%
2 10
 
0.2%
3 4
 
0.1%

Non_Cardiac_Condition
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
0
3943 
1
 
114
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 3943
97.1%
1 114
 
2.8%
2 4
 
0.1%

Length

2023-02-28T17:26:07.119604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:26:07.259151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3943
97.1%
1 114
 
2.8%
2 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3943
97.1%
1 114
 
2.8%
2 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3943
97.1%
1 114
 
2.8%
2 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3943
97.1%
1 114
 
2.8%
2 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3943
97.1%
1 114
 
2.8%
2 4
 
0.1%

Hypertension
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
2624 
0
1432 
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 2624
64.6%
0 1432
35.3%
2 5
 
0.1%

Length

2023-02-28T17:26:07.494217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:26:07.613097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2624
64.6%
0 1432
35.3%
2 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2624
64.6%
0 1432
35.3%
2 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2624
64.6%
0 1432
35.3%
2 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2624
64.6%
0 1432
35.3%
2 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2624
64.6%
0 1432
35.3%
2 5
 
0.1%

Dyslipidemia
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
2290 
0
1766 
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 2290
56.4%
0 1766
43.5%
2 5
 
0.1%

Length

2023-02-28T17:26:07.731488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:26:07.887403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2290
56.4%
0 1766
43.5%
2 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2290
56.4%
0 1766
43.5%
2 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2290
56.4%
0 1766
43.5%
2 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2290
56.4%
0 1766
43.5%
2 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2290
56.4%
0 1766
43.5%
2 5
 
0.1%

DM
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
2173 
0
1883 
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 2173
53.5%
0 1883
46.4%
2 5
 
0.1%

Length

2023-02-28T17:26:08.011642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:26:08.143259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2173
53.5%
0 1883
46.4%
2 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2173
53.5%
0 1883
46.4%
2 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2173
53.5%
0 1883
46.4%
2 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2173
53.5%
0 1883
46.4%
2 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2173
53.5%
0 1883
46.4%
2 5
 
0.1%

DM_Type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
2163 
2
1872 
0
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 2163
53.3%
2 1872
46.1%
0 26
 
0.6%

Length

2023-02-28T17:26:08.256838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:26:08.414357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2163
53.3%
2 1872
46.1%
0 26
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 2163
53.3%
2 1872
46.1%
0 26
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2163
53.3%
2 1872
46.1%
0 26
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2163
53.3%
2 1872
46.1%
0 26
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2163
53.3%
2 1872
46.1%
0 26
 
0.6%

DM_Treatment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0076336
Minimum0
Maximum9
Zeros103
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-02-28T17:26:08.541874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q39
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.446107
Coefficient of variation (CV)0.34906319
Kurtosis0.50661024
Mean7.0076336
Median Absolute Deviation (MAD)1
Skewness-1.1742262
Sum28458
Variance5.9834393
MonotonicityNot monotonic
2023-02-28T17:26:08.641121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 1873
46.1%
7 896
22.1%
5 489
 
12.0%
3 338
 
8.3%
8 164
 
4.0%
0 103
 
2.5%
1 100
 
2.5%
4 63
 
1.6%
6 34
 
0.8%
2 1
 
< 0.1%
ValueCountFrequency (%)
0 103
 
2.5%
1 100
 
2.5%
2 1
 
< 0.1%
3 338
 
8.3%
4 63
 
1.6%
5 489
 
12.0%
6 34
 
0.8%
7 896
22.1%
8 164
 
4.0%
9 1873
46.1%
ValueCountFrequency (%)
9 1873
46.1%
8 164
 
4.0%
7 896
22.1%
6 34
 
0.8%
5 489
 
12.0%
4 63
 
1.6%
3 338
 
8.3%
2 1
 
< 0.1%
1 100
 
2.5%
0 103
 
2.5%

Smoking_History
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
2464 
0
982 
2
524 
3
 
85
4
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row0
4th row4
5th row4

Common Values

ValueCountFrequency (%)
1 2464
60.7%
0 982
 
24.2%
2 524
 
12.9%
3 85
 
2.1%
4 6
 
0.1%

Length

2023-02-28T17:26:08.770668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:26:08.922471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2464
60.7%
0 982
 
24.2%
2 524
 
12.9%
3 85
 
2.1%
4 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2464
60.7%
0 982
 
24.2%
2 524
 
12.9%
3 85
 
2.1%
4 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2464
60.7%
0 982
 
24.2%
2 524
 
12.9%
3 85
 
2.1%
4 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2464
60.7%
0 982
 
24.2%
2 524
 
12.9%
3 85
 
2.1%
4 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2464
60.7%
0 982
 
24.2%
2 524
 
12.9%
3 85
 
2.1%
4 6
 
0.1%

Lipid_24_Collected
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
3455 
0
599 
2
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 3455
85.1%
0 599
 
14.8%
2 7
 
0.2%

Length

2023-02-28T17:26:09.043530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:26:09.170453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 3455
85.1%
0 599
 
14.8%
2 7
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 3455
85.1%
0 599
 
14.8%
2 7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3455
85.1%
0 599
 
14.8%
2 7
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3455
85.1%
0 599
 
14.8%
2 7
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3455
85.1%
0 599
 
14.8%
2 7
 
0.2%

Interactions

2023-02-28T17:25:58.623414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:34.392183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:36.260495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:38.208369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:40.139184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:42.095851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:44.160947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:46.069879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:48.215952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:50.341396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:52.441463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:54.560249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:56.613115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:58.768494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:34.503925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:36.385985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:38.346084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:40.284540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:42.249221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:44.306309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:46.267689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:48.403492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:50.503251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:52.600623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:54.703669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:56.779591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:58.951795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:34.629305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:36.582730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:38.524718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:40.428719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:42.415239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:44.468091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:46.415262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:48.584760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:50.666346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:52.759846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:54.859807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:56.922473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:59.123101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:34.784262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:36.746082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:38.672339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:40.614683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:42.588526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:44.609829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:46.550038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:48.773399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:50.842592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:52.946929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:55.026531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:57.072741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:59.251322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:34.903637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:36.920061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:38.822081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:40.735776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:42.738380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:44.759233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:46.702489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:48.916603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:51.009588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:53.097090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:55.199180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:57.222191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:59.403409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:35.020390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:37.065975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:38.960184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:40.905032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:42.890631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:44.908783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:46.878934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:49.073193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:51.166461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:53.258765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:55.337411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:57.385966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:59.550121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:35.173764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:37.202790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:39.096262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:41.066914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:43.030525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:45.045942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:47.077538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:49.250757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:51.332295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:53.399919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:55.509045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:57.558504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:59.701495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:35.352247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:37.328247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:39.236956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:41.202862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:43.163306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:45.193648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:47.231484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:49.390162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:51.478607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:53.541312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:55.662297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:57.716273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:59.888759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:35.506175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:37.487112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:39.401972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:41.334642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:43.358554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:45.360068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:47.382777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:49.542637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:51.635251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:53.696985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:55.819530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:57.845169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:26:00.055326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:35.645782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:37.640650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:39.568260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:41.588778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:43.536052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:45.510942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:47.531202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:49.707755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:51.807112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:53.864943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:55.968706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:58.015192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:26:00.208794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:35.906157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:37.781633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:39.697351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:41.711782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:43.678970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:45.655881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:47.801442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:49.863675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:51.955668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:54.116375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:56.125123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:58.157787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:26:00.348243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:36.015660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:37.902425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:39.826730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:41.850381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:43.830086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:45.798681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:47.925356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:50.041939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:52.121212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:54.259324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:56.295913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:58.321961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:26:00.521210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:36.151587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:38.039627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:39.986973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:41.981836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:44.003499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:45.937583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:48.055259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:50.206867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:52.288912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:54.427530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:56.460005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-28T17:25:58.485057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-28T17:26:09.310059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
AgeYear_DM_DiagnosedDM_DurationHeart_RateWaistFasting_Blood_Glucose_Value_SI_UnitsHbA1C_Admission_ValueCholesterol_Value_SI_UnitsTriglycerides_Value_SI_UnitsBMICreatinine_ClearanceEducationDM_TreatmentGenderWorkCardiac_Arrest_AdmissionNon_Cardiac_ConditionHypertensionDyslipidemiaDMDM_TypeSmoking_HistoryLipid_24_Collected
Age1.000-0.2950.3010.040-0.0980.0470.029-0.176-0.226-0.154-0.638-0.034-0.1100.1620.3270.0570.0660.2050.1210.1400.1630.1610.057
Year_DM_Diagnosed-0.2951.000-0.995-0.062-0.0400.027-0.0890.1950.092-0.1180.216-0.004-0.0670.0000.1100.0000.0000.0000.0000.0780.4990.0510.000
DM_Duration0.301-0.9951.0000.0510.044-0.0100.075-0.179-0.0720.108-0.218-0.0070.0540.0000.0000.0000.0000.0000.0220.0780.4990.0000.000
Heart_Rate0.040-0.0620.0511.000-0.0090.0930.1120.014-0.0040.047-0.0620.007-0.0980.1170.0470.1760.1580.0910.0000.1020.0900.0390.000
Waist-0.098-0.0400.044-0.0091.0000.1090.105-0.0050.1290.5090.2600.004-0.1320.1210.0550.0280.0000.1070.1180.1160.0830.0650.072
Fasting_Blood_Glucose_Value_SI_Units0.0470.027-0.0100.0930.1091.0000.6350.0240.1500.113-0.023-0.033-0.4880.0400.0000.0000.0570.0000.0220.0540.0200.0000.000
HbA1C_Admission_Value0.029-0.0890.0750.1120.1050.6351.000-0.0050.1870.1430.008-0.013-0.6000.1040.0270.0000.0000.1080.1430.4520.3180.0370.000
Cholesterol_Value_SI_Units-0.1760.195-0.1790.014-0.0050.024-0.0051.0000.3870.0340.1470.0190.1050.0230.0000.0000.0000.0000.0000.0000.0000.0000.000
Triglycerides_Value_SI_Units-0.2260.092-0.072-0.0040.1290.1500.1870.3871.0000.1550.161-0.016-0.0920.0000.0130.0000.0000.0250.0400.0180.0240.0210.059
BMI-0.154-0.1180.1080.0470.5090.1130.1430.0340.1551.0000.3820.032-0.1210.0700.0000.0160.0000.0150.0000.0130.0000.0090.000
Creatinine_Clearance-0.6380.216-0.218-0.0620.260-0.0230.0080.1470.1610.3821.0000.0330.0950.0350.2160.0000.0630.1350.0670.0640.0530.1400.000
Education-0.034-0.004-0.0070.0070.004-0.033-0.0130.019-0.0160.0320.0331.000-0.0070.3530.6550.5770.7070.6420.6330.6340.0740.4420.534
DM_Treatment-0.110-0.0670.054-0.098-0.132-0.488-0.6000.105-0.092-0.1210.095-0.0071.0000.2000.1130.0110.0190.2370.2370.7000.7130.0650.037
Gender0.1620.0000.0000.1170.1210.0400.1040.0230.0000.0700.0350.3530.2001.0000.3800.0000.0000.2070.1200.1620.1630.4330.064
Work0.3270.1100.0000.0470.0550.0000.0270.0000.0130.0000.2160.6550.1130.3801.0000.5770.7080.6550.6380.6430.1260.5020.535
Cardiac_Arrest_Admission0.0570.0000.0000.1760.0280.0000.0000.0000.0000.0160.0000.5770.0110.0000.5771.0000.7100.6320.6330.6320.0100.4710.534
Non_Cardiac_Condition0.0660.0000.0000.1580.0000.0570.0000.0000.0000.0000.0630.7070.0190.0000.7080.7101.0000.6330.6320.6330.0320.5780.535
Hypertension0.2050.0000.0000.0910.1070.0000.1080.0000.0250.0150.1350.6420.2370.2070.6550.6320.6331.0000.7600.7450.2330.6580.597
Dyslipidemia0.1210.0000.0220.0000.1180.0220.1430.0000.0400.0000.0670.6330.2370.1200.6380.6330.6320.7601.0000.7440.2300.6500.597
DM0.1400.0780.0780.1020.1160.0540.4520.0000.0180.0130.0640.6340.7000.1620.6430.6320.6330.7450.7441.0000.7010.6510.597
DM_Type0.1630.4990.4990.0900.0830.0200.3180.0000.0240.0000.0530.0740.7130.1630.1260.0100.0320.2330.2300.7011.0000.0850.004
Smoking_History0.1610.0510.0000.0390.0650.0000.0370.0000.0210.0090.1400.4420.0650.4330.5020.4710.5780.6580.6500.6510.0851.0000.654
Lipid_24_Collected0.0570.0000.0000.0000.0720.0000.0000.0000.0590.0000.0000.5340.0370.0640.5350.5340.5350.5970.5970.5970.0040.6541.000

Missing values

2023-02-28T17:26:00.864536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-28T17:26:01.334600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-28T17:26:01.738611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AgeYear_DM_DiagnosedDM_DurationHeart_RateWaistFasting_Blood_Glucose_Value_SI_UnitsHbA1C_Admission_ValueCholesterol_Value_SI_UnitsTriglycerides_Value_SI_UnitsBMICreatinine_ClearanceGenderEducationWorkCardiac_Arrest_AdmissionNon_Cardiac_ConditionHypertensionDyslipidemiaDMDM_TypeDM_TreatmentSmoking_HistoryLipid_24_Collected
062NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN111002222942
152NaN10.0NaNNaNNaNNaNNaNNaNNaNNaN111001111342
273NaNNaN75.060.07.00NaN5.381.8520.0024.12000001102901
346NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN163322222942
446NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN063322222942
5642002.010.0117.085.011.93NaN5.402.2433.2963.60040001111511
6561988.024.082.085.04.407.14.701.3031.22114.24000000111810
778NaNNaN90.090.06.205.64.111.1119.5627.55000001002910
854NaNNaN75.091.04.005.65.202.2023.0592.37120001002911
954NaNNaN90.093.0NaNNaNNaNNaN23.8173.42020000002910
AgeYear_DM_DiagnosedDM_DurationHeart_RateWaistFasting_Blood_Glucose_Value_SI_UnitsHbA1C_Admission_ValueCholesterol_Value_SI_UnitsTriglycerides_Value_SI_UnitsBMICreatinine_ClearanceGenderEducationWorkCardiac_Arrest_AdmissionNon_Cardiac_ConditionHypertensionDyslipidemiaDMDM_TypeDM_TreatmentSmoking_HistoryLipid_24_Collected
4051741987.025.088.0NaN10.88.35.141.1028.4156.50100001111111
405276NaNNaN140.070.0NaNNaNNaNNaN25.7134.66141001002900
4053611993.020.087.090.04.27.45.102.5029.7650.65152011111711
4054562004.08.062.0115.07.28.17.442.3233.12109.82020001011310
405541NaN7.0106.079.0NaNNaN5.901.9027.9788.77101001111721
4056752001.011.098.092.07.58.84.672.2223.5145.28120001111511
4057561996.015.0120.093.07.911.66.661.5329.3983.77100200111701
4058552006.0NaN109.072.09.211.63.950.8022.8489.61140000011701
405977NaNNaN100.085.0NaNNaN6.710.6522.0431.00120001002911
4060731987.025.0100.0117.0NaNNaN4.101.7729.0167.36120001111801